OBJECTIVE

Insulin delivery methods, glucose-monitoring modalities, and related outcomes were examined in a large, international, diverse cohort of children and adolescents with type 1 diabetes from the Better Control in Pediatric and Adolescent Diabetes: Working to Create Centers of Reference (SWEET) -Registry.

RESEARCH DESIGN AND METHODS

Participants with type 1 diabetes of ≥1 year, aged ≤18 years, and who had documented pump or sensor usage during the period August 2017–July 2019 were stratified into four categories: injections–no sensor (referent); injections + sensor; pump–no sensor; and pump + sensor. HbA1c and proportion of patients with diabetic ketoacidosis (DKA) or severe hypoglycemia (SH) were analyzed; linear and logistic regression models adjusted for demographics, region, and gross domestic product per capita were applied.

RESULTS

Data of 25,654 participants were analyzed. The proportions of participants (adjusted HbA1c data) by study group were as follows: injections–no sensor group, 37.44% (8.72; 95% CI 8.68–8.75); injections + sensor group, 14.98% (8.30; 95% CI 8.25–8.35); pump–no sensor group, 17.22% (8.07; 95% CI 8.03–8.12); and pump + sensor group, 30.35% (7.81; 95% CI 7.77–7.84). HbA1c was lower in all categories of participants who used a pump and/or sensor compared with the injections–no sensor treatment method (P < 0.001). The proportion of DKA episodes was lower in participants in the pump + sensor (1.98%; 95% CI 1.64–2.48; P < 0.001) and the pump–no sensor (2.02%; 95% CI 1.64–2.48; P < 0.05) groups when compared with those in the injections–no sensor group (2.91%; 95% CI 2.59–3.31). The proportion of participants experiencing SH was lower in pump–no sensor group (1.10%; 95% CI 0.85–1.43; P < 0.001) but higher in the injections + sensor group (4.25%; 95% CI 3.65–4.95; P < 0.001) compared with the injections–no sensor group (2.35%; 95% CI 2.04–2.71).

CONCLUSIONS

Lower HbA1c and fewer DKA episodes were observed in participants using either a pump or continuous glucose monitoring (CGM) or both. Pump use was associated with a lower rate of SH. Across SWEET centers, use of pumps and CGM is increasing. The concomitant use of pump and CGM was associated with an additive benefit.

The Diabetes Control and Complications Trial established the importance of attaining optimal glycemic control in participants with type 1 diabetes mellitus to delay and potentially avoid long-term diabetes complications (1). Advances in pharmacology with newer insulin analogs and technology, including insulin delivery via pump and continuous glucose monitoring (CGM) offer greater options and flexibility for diabetes management. Improved therapies to manage diabetes are increasingly being used to achieve hemoglobin A1C (HbA1c) targets and also to avoid acute complications of severe hypoglycemia (SH) and diabetic ketoacidosis (DKA). Selection of treatment modality may depend on clinical indications, patient personal preferences, variable costs, or reimbursement models.

Although the efficacy and safety of different insulin analogs, pumps, and sensors have been established in clinical trials and meta-analyses (25), data from different registry-based studies have shown that only a minority of youths are able to achieve the recommended HbA1c targets despite the increasing use of such technology (68). Data from the T1D Exchange Registry in the U.S. suggest that the concomitant use of pumps and sensors may have an additive beneficial effect on HbA1c levels respective to when they are used separately, both in adult and pediatric populations (7). This contrasts with findings from a prospective, real-world, single-center, 3-year study suggesting that glycemic outcomes in adults are more influenced by the use of CGM than by the insulin-delivery method (9).

In pediatric populations, use of pumps and CGM vary across health care delivery contexts internationally, and substantial differences have been observed in HbA1c outcomes. In some countries these outcomes are well established (10,11). Insulin delivery method and CGM use varies widely depending on the geographic area and especially the characteristics of the different health care systems. Accordingly, the evaluation of clinical outcomes and treatment modalities in international multicenter registries is essential to determine the impact of diabetes technology on the achievement of glycemic targets and the occurrence of SH and DKA worldwide. In this study, we examined the association between different treatment modalities and clinical outcomes in a large, diverse, international cohort of children and adolescents with type 1 diabetes from the Better Control in Pediatric and Adolescent Diabetes: Working to Create Centers of Reference (SWEET) registry.

SWEET is a multinational network of centers providing care for children, adolescents, and young adults with diabetes mellitus. Its mission is to harmonize diabetes care to optimize clinical outcomes and thereby establish standards of care in pediatric diabetes worldwide (12). The centers participating in the SWEET registry submit a set of standardized data to the Institute of Epidemiology and Medical Biometry, Ulm University, in Ulm, Germany, biannually either through the Diabetes Patienten Verlaufskodocumentation (DPV)-for-SWEET (Diabetes Patienten Verlaufskodocumentation) software (https://sweet.zibmt.uni-ulm.de/software.php) developed at Ulm University, from national registries, or through local clinical electronic health records. Data plausibility is validated by the team at Ulm University; if inconsistent or missing data are present, a request for correction is sent to the corresponding submitting center. All contributing centers fulfill current regulatory data protection security and ethics compliance. SWEET centers are present in five geographic hubs: Europe, Asia/Middle East/Africa, Australia/New Zealand, North America, and South America.

As of October 2019, the SWEET database contained information on 66,421 participants from 101 centers worldwide. For the final analysis of this study, 25,654 participants with type 1 diabetes who were aged ≤18 years and had diabetes duration ≥1 year with documented pump or sensor status during August 2017–July 2019 were included (Fig. 1). For each participant, data were aggregated for the 2-year observation period.

Figure 1

Flowchart for selection of the study population from the SWEET registry. T1DM, type 1 diabetes.

Figure 1

Flowchart for selection of the study population from the SWEET registry. T1DM, type 1 diabetes.

Close modal

Age, sex, diabetes duration, age at diabetes onset, number of self-monitoring blood glucose (SMBG) tests, total daily insulin dose per kilogram of body weight, type of insulin administration (pump or injections), type of glucose monitoring (CGM: yes/no), HbA1c, number of SH episodes, and number of hospitalizations due to DKA were documented in the SWEET database. Each participant was assigned to their corresponding geographic SWEET region, gross domestic product (GDP)-per-capita, and GDP–health expenditure-per-capita of the corresponding country. GDP-per-capita data were based on World bank data indicators from 2018 (13) and expressed in current US dollars; GDP health expenditure was based on data from 2016 (14) and also expressed in current US dollars. In addition, centers were grouped in clusters according to health system similarities, geographic area and reimbursement policies.

HbA1c was measured locally and standardized to the Diabetes Control and Complications Trial reference of 20–42 mmol/mol (4%–6%) (1,15). Participants were defined as insulin-pump or CGM users when they were using the respective device at least at one visit during the observation period. Both real-time CGM and intermittent CGM were considered within the CGM category. SH and DKA were defined according to International Society of Pediatric and Adolescent Diabetes (ISPAD) Clinical Consensus Guidelines (16,17) and expressed in proportion of episodes during the entire observation period.

Participants were categorized into four groups depending on the insulin delivery method and the use of CGM: 1) injections–no sensor; 2) injections + sensor; 3) pump–no sensor; and 4) pump + sensor. For the participants in the pump + sensor category, the proportion of participants using a sensor-augmented pump was also estimated. The category sensor-augmented pump was defined as any pump that integrates CGM data and incorporates any kind of automation, such as insulin suspension on low, before low, or hybrid closed loop. Because of the small sample of participants in this group, no additional analysis was performed. Use of each category was analyzed according to the following age groups: <5 years, 5–10 years, and 10–18 years.

For each of the four categories, mean HbA1c and the proportion of participants achieving both the former ISPAD HbA1c target of <7.5% (<58 mmol/mol) (18) and the current ISPAD HbA1c target of <7.0% (<53 mmol/mol) (19) was calculated. The percentages of SH and DKA episodes (percentage of participants having one episode of DKA or SH during the 2-year aggregated period) were determined for each treatment category. HbA1c and percentages of DKA and SH episodes were calculated for participants in different age groups: <12 years, 12–16 years, and >16 years.

### Statistical Analysis

Continuous variables were described as median, quartile 1 (Q1), and quartile 3 (Q3). Categorical variables were presented as percentages. The Kruskal-Wallis test was used to compare continuous variables and χ2 test to compare binary variables. To adjust for multiple testing, P values were corrected by the Bonferroni-Holm method. To examine the potential differences, the injections–no sensor group was used as the referent for establishing comparisons. HbA1c (continuous variable) and proportion of SH and DKA episodes (binary variable) were adjusted, respectively, using linear and logistic regression models. The following models were applied: model 1 was adjusted for age at onset, sex, and diabetes duration. Model 2 was additionally adjusted for region to account for variations among regions. Model 3 was adjusted for demographics and GDP per capita. In model 4, demographics and GDP health expenditure per capita data were used. In addition, an adjusted sex-specific comparisons regression model for HbA1c <7.0% (<53 mmol/mol), proportion of DKA episodes, and proportion of SH episodes between the reference group and the rest of the treatment modality categories was used.

Results were presented as means with 95% CIs. To adjust for multiple group comparisons, Turkey-Kramer test was used. Two-sided P values <0.05 were defined as statistically significant. All analyses were performed with SAS, version 9.4 (SAS Institute Inc., Cary, NC).

The final study population comprised 25,654 people with type 1 diabetes from 101 centers participating in the pediatric diabetes network SWEET (median age, 13.80 [Q1; Q3: 10.60; 16.40,] years; males, 51.41%; diabetes duration, 5.18 [Q1; Q3: 2.99; 8.25] years). The distribution of treatment modality was as follows: injections–no sensor, 37.44%; injections + sensor, 14.98%; pump–no sensor, 17.22%; and pump + sensor 30.35%. Among those participants using a pump + sensor, 8.0% were using a sensor-augmented pump.

The pump + sensor modality was the most frequently used in the age group <5 years. For the age groups 5–9.9 years and 10–18 years, the most frequently used modality was injections–no sensor, followed by pump + sensor. For all age groups, the injections + sensor modality was the least common except for the age group 10–18 years, in which it was the pump–no sensor modality (Fig. 2).

Figure 2

The distribution of treatment modality by age group. Each bar represents a treatment modality. Black bars denote injection-no sensor group; Light grey bars denote injection+sensor group; Striped bars denote pump-no sensor group. White bars with dots represent pump+sensor group. Data are number of patients.

Figure 2

The distribution of treatment modality by age group. Each bar represents a treatment modality. Black bars denote injection-no sensor group; Light grey bars denote injection+sensor group; Striped bars denote pump-no sensor group. White bars with dots represent pump+sensor group. Data are number of patients.

Close modal

Demographic features across treatment modality groups are described in Table 1. Across these groups, significant differences were observed in sex, age, age at diabetes onset, diabetes duration, number of SMBG tests, insulin requirements, GDP per capita, health investment on GDP per capita, and region distribution (P < 0.001).

Table 1

Demographics across treatment modality groups (unadjusted results)

Total no. (N = 25,654)Injections–no sensor (reference group; n = 9,606)Injections + sensor group (n = 3843)Pump–no sensor group (n = 4418)Pump + sensor group (n = 7787)
Male sex (%) 25,654 51.37 54.49 49.82 50.78
Age, years  14.10 13.70 14.50 13.10
(10.80; 16.60) (10.50; 16.30) (11.40; 17.00) (9.90; 15.80)
Age at onset, years  7.90 7.90 6.30 6.00
(4.60; 11.00) (4.70; 11.00) (3.50; 9.50) (3.30; 9.20)
Diabetes duration, years  4.61 4.25 6.59 5.58
(2.60; 7.51) (2.42; 7.28) (3.98; 9.66) (3.41; 8.62)
SMBG, number/day 22,073 4.00 3.80 5.05 4.10
(2.00; 5.00) (2.00; 5.00) (4.00; 7.14) (2.50; 6.00)
Daily insulin dose, units/kg 23,684 0.90 0.84 0.82 0.80
(0.70; 1.00) (0.66; 1.01) (0.68; 0.97) (0.67; 0.95)
GDP per capita, USD 25,654 15,424 41,966 46,211 54,112
(3,238; 42,491) (23,146; 57,305) (23,079; 60,726) (34,318; 62,641)
Health investment on GDP per capita, USD 25,654 696 3,274 3,274 3,534
Region (%)  (80; 3,274) (1,195; 3,992) (918; 4,862) (2,034; 8,078)
Europe 12,840 39 59 68 48
Asia/Middle East/Africa 3,890 36
Australia/New Zealand 1,646 10 10
North America 6,590 15 25 24 40
South America 688
HbA1c <58 mmol/mol (<7.5%), (%) 25,253 26.32 39.68* 45.78* 44.11*
HbA1c <53 mmol/mol (<7%), (%) 25,253 14.58 24.08* 27.89* 26.47*
Participants <12 years age with HbA1c <58 mmol/mol (<7.5%), (%) 8,811 28.59 46.17* 58.80* 54.17*
Participants <12 years old with HbA1c <53 mmol/mol (<7%), (%) 8,811 15.26 29.88* 38.56* 33.66*
Participants 12–16 years old with HbA1c <58 mmol/mol (<7.5%), (%) 8,901 24.96 36.12* 44.70* 39.09*
Participants 12–16 years old with HbA1c <53 mmol/mol (<7%), (%) 8,901 14.25 20.96* 25.93* 23.04*
Participants >16 years old with HbA1c <58 mmol/mol (<7.5%), (%) 7,541 25.36 35.82* 36.03* 33.82*
Participants >16 years old with HbA1c <53 mmol/mol (<7%), (%) 7,541 14.25 20.99* 21.04* 19.49*
Total no. (N = 25,654)Injections–no sensor (reference group; n = 9,606)Injections + sensor group (n = 3843)Pump–no sensor group (n = 4418)Pump + sensor group (n = 7787)
Male sex (%) 25,654 51.37 54.49 49.82 50.78
Age, years  14.10 13.70 14.50 13.10
(10.80; 16.60) (10.50; 16.30) (11.40; 17.00) (9.90; 15.80)
Age at onset, years  7.90 7.90 6.30 6.00
(4.60; 11.00) (4.70; 11.00) (3.50; 9.50) (3.30; 9.20)
Diabetes duration, years  4.61 4.25 6.59 5.58
(2.60; 7.51) (2.42; 7.28) (3.98; 9.66) (3.41; 8.62)
SMBG, number/day 22,073 4.00 3.80 5.05 4.10
(2.00; 5.00) (2.00; 5.00) (4.00; 7.14) (2.50; 6.00)
Daily insulin dose, units/kg 23,684 0.90 0.84 0.82 0.80
(0.70; 1.00) (0.66; 1.01) (0.68; 0.97) (0.67; 0.95)
GDP per capita, USD 25,654 15,424 41,966 46,211 54,112
(3,238; 42,491) (23,146; 57,305) (23,079; 60,726) (34,318; 62,641)
Health investment on GDP per capita, USD 25,654 696 3,274 3,274 3,534
Region (%)  (80; 3,274) (1,195; 3,992) (918; 4,862) (2,034; 8,078)
Europe 12,840 39 59 68 48
Asia/Middle East/Africa 3,890 36
Australia/New Zealand 1,646 10 10
North America 6,590 15 25 24 40
South America 688
HbA1c <58 mmol/mol (<7.5%), (%) 25,253 26.32 39.68* 45.78* 44.11*
HbA1c <53 mmol/mol (<7%), (%) 25,253 14.58 24.08* 27.89* 26.47*
Participants <12 years age with HbA1c <58 mmol/mol (<7.5%), (%) 8,811 28.59 46.17* 58.80* 54.17*
Participants <12 years old with HbA1c <53 mmol/mol (<7%), (%) 8,811 15.26 29.88* 38.56* 33.66*
Participants 12–16 years old with HbA1c <58 mmol/mol (<7.5%), (%) 8,901 24.96 36.12* 44.70* 39.09*
Participants 12–16 years old with HbA1c <53 mmol/mol (<7%), (%) 8,901 14.25 20.96* 25.93* 23.04*
Participants >16 years old with HbA1c <58 mmol/mol (<7.5%), (%) 7,541 25.36 35.82* 36.03* 33.82*
Participants >16 years old with HbA1c <53 mmol/mol (<7%), (%) 7,541 14.25 20.99* 21.04* 19.49*

Data are given as median with quartiles [Q1; Q3] unless otherwise stated. Binary variables are expressed as percentages.

*

Significantly different from the reference group at P < 0.001.

The distribution of study population origins were as follows: Europe (50%), North America (26%) Asia/Middle East/Asia (15%), Australia/New Zealand (6%), and South America (3%). The use of different treatment modalities varied across centers and regions. Grouping the countries on the basis of similar health care systems showed differences in the distribution of different treatment modalities (Supplementary Table 1). The GDP per capita and health investment on GDP for participants using technology components (i.e., pump with or without sensor and injections + sensor) were higher than for those using injections–no sensor.

In the whole cohort, 37% of participants attained HbA1c <7.5% (58 mmol/mol) and 21% achieved HbA1c <7.0% (53 mmol/mol). The proportion of participants achieving both targets was significantly higher in those groups using technology. The HbA1c target <7.5% (<58% mmol/mol) was achieved by 44% of participants aged <12 years, 34% of those aged 12–16 years, and 31% of those aged >16 years. The HbA1c target <7.0% (<53% mmol/mol) was achieved by 27% of participants aged <12 years, 20% of those aged 12–16 years, and 18% of participants >16 years.

Differences among participants with technology components and the reference group for unadjusted values of HbA1c, DKA, and SH episodes were found when the participants were stratified by age groups (Supplementary Table 2).

The results for linear and logistic regression analyses are shown in Table 2. Adjusted for demographics (model 1), HbA1c was significantly lower for all treatment modality groups when compared with the reference group. Even after adjustment for demographics and region (model 2); demographics, region, and GDP per capita (model 3); and demographics, region, and GDP–health per capita (model 4), similar results were found.

Table 2

HbA1c linear regression model and DKA and SH logistic models

Injections–no sensor (reference group)Injections + sensor groupPump–no sensor groupPump + sensor group
HbA1c by model
1
HbA1c (mmol/mol) 72.7 (72.4–73.1) 65.3 (64.7–65.9)** 62.8 (62.3–63.3)** 62.6 (62.2–63.0)**
HbA1c, model (%) 8.80 (8.77–8.84) 8.12 (8.07–8.18) 7.89 (7.84–7.94) 7.88 (7.84–7.91)
2
HbA1c (mmol/mol) 71.6 (71.2–72.1) 66.7 (66.1–67.2)** 65.1 (64.6–65.6)** 62.1 (61.8–62.5)**
HbA1c, model (%) 8.69 (8.66–8.69) 8.25 (8.20–8.30) 8.10 (8.06–8.15) 7.83 (7.80–7.87)
3
HbA1c (mmol/mol) 71.9 (71.5–72.3) 67.1 (66.5–67.6)** 64.8 (64.3–65.3)** 61.7 (61.3–62.1)**
HbA1c, model (%) 8.73 (8.69–8.76) 8.29 (8.24–8.34) 8.08 (8.03–8.12) 7.80 (7.76–7.83)
4
HbA1c (mmol/mol) 71.8 (71.4–72.2) 67.2 (66.7–67.8)** 64.7 (64.2–65.2)** 61.8 (61.4–62.2)**
HbA1c, model (%) 8.72 (8.68–8.75) 8.30 (8.25–8.35) 8.07 (8.03–8.12) 7.81 (7.77–7.84)
DKA by model (%)
1 3.49 (3.14–3.69) 3.09 (2.59–3.69) 2.05 (1.68–2.50)** 2.67 (2.33–3.05)*
2 2.89 (2.54–3.29) 2.87 (2.54–3.29) 2.08 (1.69–2.55)* 2.08 (1.79–2.42)**
3 2.92 (2.58–3.35) 2.84 (2.36–3.42) 2.03 (1.65–2.50)* 1.99 (1.70–2.32)**
4 2.91 (2.59–3.31) 2.87 (2.34–3.45) 2.02 (1.64–2.48)* 1.98 (1.64–2.48)**
SH by model (%)
1 2.19 (1.91–2.50) 4.78 (4.15–5.52)** 1.18 (1.12–1.53)** 2.56 (2.23–2.93)
2 2.37 (2.06–2.73) 4.58 (3.95–5.31)** 1.13 (0.97–1.47)** 2.31 (2.00–2.68)
3 2.22 (1.92–2.56) 4.06 (3.48–4.73)** 1.11 (0.86–1.45)** 2.24 (1.93–2.60)
4 2.35 (2.04–2.71) 4.25 (3.65–4.95)** 1.10 (0.85–1.43)** 2.17 (1.86–2.52)
Injections–no sensor (reference group)Injections + sensor groupPump–no sensor groupPump + sensor group
HbA1c by model
1
HbA1c (mmol/mol) 72.7 (72.4–73.1) 65.3 (64.7–65.9)** 62.8 (62.3–63.3)** 62.6 (62.2–63.0)**
HbA1c, model (%) 8.80 (8.77–8.84) 8.12 (8.07–8.18) 7.89 (7.84–7.94) 7.88 (7.84–7.91)
2
HbA1c (mmol/mol) 71.6 (71.2–72.1) 66.7 (66.1–67.2)** 65.1 (64.6–65.6)** 62.1 (61.8–62.5)**
HbA1c, model (%) 8.69 (8.66–8.69) 8.25 (8.20–8.30) 8.10 (8.06–8.15) 7.83 (7.80–7.87)
3
HbA1c (mmol/mol) 71.9 (71.5–72.3) 67.1 (66.5–67.6)** 64.8 (64.3–65.3)** 61.7 (61.3–62.1)**
HbA1c, model (%) 8.73 (8.69–8.76) 8.29 (8.24–8.34) 8.08 (8.03–8.12) 7.80 (7.76–7.83)
4
HbA1c (mmol/mol) 71.8 (71.4–72.2) 67.2 (66.7–67.8)** 64.7 (64.2–65.2)** 61.8 (61.4–62.2)**
HbA1c, model (%) 8.72 (8.68–8.75) 8.30 (8.25–8.35) 8.07 (8.03–8.12) 7.81 (7.77–7.84)
DKA by model (%)
1 3.49 (3.14–3.69) 3.09 (2.59–3.69) 2.05 (1.68–2.50)** 2.67 (2.33–3.05)*
2 2.89 (2.54–3.29) 2.87 (2.54–3.29) 2.08 (1.69–2.55)* 2.08 (1.79–2.42)**
3 2.92 (2.58–3.35) 2.84 (2.36–3.42) 2.03 (1.65–2.50)* 1.99 (1.70–2.32)**
4 2.91 (2.59–3.31) 2.87 (2.34–3.45) 2.02 (1.64–2.48)* 1.98 (1.64–2.48)**
SH by model (%)
1 2.19 (1.91–2.50) 4.78 (4.15–5.52)** 1.18 (1.12–1.53)** 2.56 (2.23–2.93)
2 2.37 (2.06–2.73) 4.58 (3.95–5.31)** 1.13 (0.97–1.47)** 2.31 (2.00–2.68)
3 2.22 (1.92–2.56) 4.06 (3.48–4.73)** 1.11 (0.86–1.45)** 2.24 (1.93–2.60)
4 2.35 (2.04–2.71) 4.25 (3.65–4.95)** 1.10 (0.85–1.43)** 2.17 (1.86–2.52)

Linear regression model was applied for HbA1c and logistic models were used for DKA and SH. Adjusted means with 95% CIs are shown for linear models. Logistic model estimates with 95% CIs: model 1 was adjusted for demographics (sex, age at onset and diabetes duration); model 2: was adjusted for demographics and region; model 3 was adjusted for demographics, region, and GDP per capita; and model 4 was adjusted for demographics, region, and health investment on GDP per capita.

*

Significantly different from the reference group at P < 0.05.

**

Significantly different from the reference group at P < 0.01.

The proportion of DKA episodes was significantly lower only in participants using a pump (with or without sensor) when compared with the reference group (Table 2), although the significance varied across the different models. The proportion of SH episodes was significantly lower only in the pump–no sensor group, compared with the reference group. The proportion of SH events was significantly higher in the injections + sensor group. No differences were found in terms of SH between the with pump + sensor group and the injections–no sensor group. These results did not vary after adjusting for the different models.

### Sex-Specific Analyses

Odds ratios (ORs) for the proportions of DKA hospitalizations, SH episodes, and participants with HbA1c <7.0% (<53 mmol/mol) were calculated for boys and girls separately in subsequent comparisons between the reference group and the rest of treatment modalities (Supplementary Fig. 1). The ORs of boys and girls were similar for all three comparisons with the referent, except for the proportion of participants with HbA1c <7.0% (<53 mmol/mol) in the pump–no sensor group (Supplementary Fig. 1B) and in the pump + sensor group (Supplementary Fig. 1C), where the ORs were higher for girls.

This study comprised a large, diverse, international cohort of children and adolescents with type 1 diabetes and demonstrated that the management of type 1 diabetes has diversified, and the use of insulin pumps and CGM is increasing. Although the most frequently used combination during the study was still injections and SBGM, our study shows that >60% of participants were using at least one technological component for diabetes management (CGM or pump). Those using a pump and CGM concomitantly were observed to be more likely to achieve glycemic targets.

The increasing use of technology is consistent with data described by other registry-based studies. We report 48% of all participants younger than 18 years used insulin pumps, compared with 44% reported in a 2016 SWEET study (20). The DPV registry recently reported that Germany and Austria have pump therapy rates of 39% in participants younger than 20 years, with a net increase of 52% occurring during the period 1995–2017 (21). Scandinavian registries show a higher use of pumps (22,23), whereas lower uptake has been observed in the England and Wales registry (24) and in Ireland (25).

Our study showed 45% of participants used CGM. This proportion is slightly higher than 2017 data reported by the T1D Exchange registry (26) and significantly higher than reported by DPV registry (21). We note that the period of the studies (2017–2019) coincided with increasing access to and reimbursement for CGM in European countries, especially in the pediatric population. In a previous SWEET study (27), we reported that the vast majority of European countries had recently started CGM reimbursement, contrasting with Sweden and Slovenia, where CGM has been reimbursed for more than a decade. In parallel, the reimbursement of intermittent scanning CGM rose substantially during the period 2017–2019 across Europe, where funding for this technology became available in >30 countries. The U.S. has also increased CGM use in the pediatric population with improved insurance coverage (26,28). In the present study, we did not distinguish between the type of CGM (i.e., real-time or intermittent scanning CGM), but we speculate that the increase in CGM use among European centers is based primarily on a growing use of intermittent scanning CGM, whereas in North America, it is based on real-time CGM.

The majority of participants using a pump, with or without CGM, were affiliated with European or North American centers included in the SWEET database, whereas those using injections alone without CGM were more often located in Europe and Asia/Middle East/Africa centers. Interestingly, when we grouped countries on the basis of similarities in health care systems, we observed differences in the use of treatment modalities, with a higher proportion of concomitant use of pump and CGM in participants belonging to centers in the U.S., Australia/Canada/New Zealand and Northern Europe/Slovenia. It is noteworthy that treatment modality distribution varied between the Southern and Eastern Europe and between Northern Europe and Slovenia centers, with a greater use of pumps, with or without sensor, in participants in Northern Europe and Slovenia and a higher proportion of injections and CGM use in participants in the Southern and Eastern Europe group. Among participants in Asia/Middle East/Africa and South America, most used insulin injections without CGM. This reflects that access to diabetes device technology is likely related to more favorable reimbursement policies (2730) and how robust the economy is in these countries. Nevertheless, SWEET is a network of reference centers and perhaps these data may not reflect, accordingly, the reality of pump and CGM access in each country.

We noted the treatment modality varied across the different age groups. In those younger than 5 years, the most frequently used modality was the concomitant use of pump and sensor. This may be related to the hypoglycemia vulnerability in younger children and the care burden experienced by parents and caregivers in the day-to-day management of diabetes (31). Such management is consistent with current recommendations of ISPAD (32). Interestingly, among those children aged 5–18 years, the concomitant use of pump and sensor was the second most used treatment modality after the injections without a sensor. To our knowledge, this observation has not yet been described and highlights that diabetes management is evolving, with a focus on technology.

The percentage of participants attaining the previous ISPAD target of HbA1c <7.5% contrasts to that published in individual countries. The percentage is higher than in Wales, the U.S., England, and Norway; similar to Denmark, and lower than the percentages reported for Austria, Germany, and Sweden (33). This demonstrates the diversity of SWEET centers. This finding is consistent with a recent description of two contemporary diabetes cohorts from T1D Exchange and DPV, which showed large discrepancies in glycemic control between these registries, although the trajectories in life span of HbA1c may describe a similar pattern (34). Despite the increased use of technology, in this study we observed a similar percentage of participants attaining the former ISPAD HbA1c target as reported by SWEET researchers previously (35). Our study is first registry-based study, to our knowledge, to provide data describing the proportion of participants attaining the current ISPAD target <7.0%. As anticipated, participants younger than 12 years had higher rates of achieving both former and current ISPAD HbA1c targets than did participants aged 12–16 years and those older than 16 years.

Furthermore, we found that the group with more technology components (i.e., who used injections with sensor and a pump with or without sensor) had the lowest HbA1c when compared with the group using no technology (i.e., injections–no sensor). Those differences persisted when the participants were categorized by age groups. Interestingly, our finding that HbA1c in the group of pump–no sensor was slightly lower than in those in the injection + sensor group, suggests a greater impact of the pump on HbA1c as compared with the CGM in this cohort. This is in contrast to observations from 2018 by DeSalvo et al. (11), who described in a registry-based study that participants using injections and a sensor had lower HbA1c than participants using a pump and SMBG tests. In a recent prospective clinical trial (9), researchers found that injections and sensor modality may be equivalent to sensor-augmented pumps and superior to the pump and SBGM modality. In many countries, CGM is more accessible than pumps. This may confound results because pump treatment might be selectively used for candidates more likely to achieve better results. Nevertheless, the pump effect on the lower HbA1c should be interpreted with caution. HbA1c alone does not reflect glycemic excursions or time spent in hypoglycemia. Those using CGM may have reduced hypoglycemia frequency, which may result in a higher HbA1c.

Some studies have found that socioeconomic background, minority status, and parental level of education may influence not only the access but also the outcomes of pump and CGM users (3637). In our study, we were unable to account for those variables, but the adjustment of the HbA1c variable by economic in-country parameters, GDP, and GDP per capita showed no differences with respect to the unadjusted analyses.

Hospitalizations due to DKA were less frequent in pump users, independent of sensor use. In a previous multinational, registry-based study with a large number of pediatric participants, pump use was not significantly associated with DKA (39). A more recent study from the T1D Exchange registry found that pump use was associated with a lower rate of DKA (7). Our results are also aligned with contemporary observations that in participants using pump therapy, the proportion of hospitalizations due to DKA is lower than for those whose diabetes is managed with multiple daily injections (40). These findings differ from first follow-up studies focused on pump therapy that described a higher risk of DKA with this treatment modality (41). The inclusion of structured education programs and a tailored selection of the best candidates for pump therapy may explain the change (42). The effect of CGM on preventing DKA seems to be additive to the effect of pump therapy, with the lowest DKA rate observed in the combined pump _ sensor group. The impact of CGM on DKA is currently not sufficiently described, although observational studies from the U.S. and Germany suggest fewer DKA episodes in CGM users than in nonusers (7,37) and among those with early initiation of CGM within the first year of new-onset diabetes diagnosis (43).

An unexpected finding in our study was that, when compared with the modality of injections without a sensor, only the group of pump–no sensor had a lower incidence of SH. We expected an association between CGM use and lower incidence of hypoglycemia. The association of pump therapy and lower rates of SH has been well established separately in the T1D Exchange and DPV registries (7,40). In addition, in the present study, the injection + sensor group had a considerably higher frequency of SH episodes than in the injections no–sensor group. We can hypothesize that residual confounding, either due to the 2-year period of aggregated data we assigned the treatment category on the basis of last documented visit, or preferential use of sensors in patients at risk for SH (indication bias) may explain these findings. Another potential reason for an increased incidence of SH with CGM use may relate to the user’s or caregiver’s ability to view changes in sensor glucose levels in real time and be alerted when the glucose levels are high or rising rapidly. For patients or families who are strongly motivated to maintain normoglycemia, especially avoiding postprandial glucose excursions, this may lead to an assertive or premature response, resulting in a potential over-bolus of insulin. In a survey-based study of adolescents, responders stated that when the CGM device showed two arrows up, they increased their correction bolus by 140% on average (44). This self-management behavior should be discussed as part of initial and ongoing quality CGM education. The absence of alerts in some CGM models also may be influencing this observation. Interestingly, we observed differences in SH episodes for the different age groups only in the injection-sensor category, with a higher proportion of SH in the 12–16 years and >16 years age groups. These results should be interpreted with caution because the number of observed events is low and results are unadjusted.

We did not observe a sex effect on clinical outcomes related to the different treatment modalities, except for a slightly higher probability of attaining HbA1c <7.0% (<53 mmol/mol) among girls who used a pump (with or without sensor) compared with girls who did not use this treatment. To our knowledge, no studies on different treatment modalities related outcomes focused on sex have been performed in a pediatric population.

A limitation in our analyses is that we were not able to describe the proportion of participants within the category of pump + sensor that were using advanced features of the system (e.g., hybrid closed loop), because the sample size was inadequate to establish comparisons as a single group. This information may be underreported in the SWEET database. To our knowledge, studies performed by other registry networks have not incorporated this information either. Another study limitation was that we were unable to provide time-in-range data, because this parameter was not recorded during the study period. We recognize that the categorization of participants according to health care–based country groups, type of center, or geographic hub may present concerns because health care and reimbursement policies, although similar among several countries or centers, are not completely identical to establish unequivocal and objective clusters.

Despite these limitations, our study has several strengths. We report on the global distribution of technology based on data from a very large number of participants followed in many centers from a variety of countries around the world. In addition, the adjusted models incorporated socioeconomic parameters such as GDP and GDP health investment to decrease potential geographic bias.

In conclusion, we found that children and adolescents in SWEET centers who use a pump or/and sensor to manage diabetes have a lower HbA1c and fewer episodes of DKA. Also, pump users experience a lower rate of SH episodes. Our findings show a major evolution in technology use over the past 10 years and a progressive improvement in the attaining of glycemic targets globally.

Acknowledgments. The authors thank the following individuals for their support of this work: Katharina Fink and Anke Schwandt for the data management; Andreas Hungele and Ramona Ranz for the DPV software (all of Ulm University, Germany); Michael Witsch (Centre Hospitalier de Luxembourg, Luxembourg); Thomas Danne and Olga Kordonouri (Kinder- und Jugendkrankenhaus AUF DER BULT, Hannover, Germany) for center integration; Katharina Klee (Kinder- und Jugendkrankenhaus AUF DER BULT) for initiating the SWEET collaboration; and Reinhard Holl (Ulm University) for invaluable support. Finally, the authors thank all participating centers of the SWEET network, especially the collaboration centers in this investigation (Appendix).

Duality of Interest. This work was supported by the SWEET corporate members, namely, Abbott, Boehringer Ingelheim, Dexcom, Insulet, Eli Lilly and Company, Medtronic, and Sanofi. No other potential conflicts of interest relevant to this article were reported.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the corporate members.

Author Contributions. R.C.-H. designed the study, participated in data interpretation, and wrote the manuscript. A.S. designed the study, performed the statistical analysis, participated in data interpretation, and reviewed and edited the manuscript. H.A., H.B., A.C., N.C., S.C., D.G., P.G., Z.I., K.N., S.M.P.O., and C.J. researched data and reviewed and edited the manuscript. All authors approved the final version of the manuscript. R.C.-H., A.S., and C.J. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in the oral presentation session of the 13th International Conference on Advanced Technologies & Treatments for Diabetes, Madrid, Spain, 19–22 February 2020.

1.
Nathan
DM
,
Genuth
S
,
Lachin
J
, et al.;
Diabetes Control and Complications Trial Research Group
.
The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus
.
N Engl J Med
1993
;
329
:
977
986
.
2.
Siebenhofer
A
,
Plank
J
,
Berghold
A
,
Narath
M
,
Gfrerer
R
,
Pieber
T
.
Short acting insulin analogues versus regular human insulin in patients with diabetes mellitus
. In
The Cochrane Database of Systematic Reviews
.
The Cochrane Collaboration
, Ed.
Chichester, U.K.
,
John Wiley & Sons, Ltd
,
2004
, p.
CD003287
.
Accessed 21 November 2020. Available from https://doi.wiley.com/10.1002/14651858.CD003287.pub2
3.
Kaiserman
K
,
Jung
H
,
I
,
Karges
B
,
Polak
M
,
Rosilio
M
.
20 Years of insulin lispro in pediatric type 1 diabetes: a review of available evidence
.
Pediatr Diabetes
2017
;
18
:
81
94
4.
Misso
ML
,
Egberts
KJ
,
Page
M
,
O’Connor
D
,
Shaw
J
.
Continuous subcutaneous insulin infusion (CSII) versus multiple insulin injections for type 1 diabetes mellitus
. In
The Cochrane Database of Systematic Reviews
.
Cochrane Metabolic and Endocrine Disorders Group
, Ed.,
Chichester, U.K.
,
John Wiley & Sons, Ltd
,
2010
, pp.
CD005103
.
Accessed 19 March 2020. Available from https://doi.wiley.com/10.1002/14651858.CD005103.pub2
5.
Pickup
JC
,
Freeman
SC
,
Sutton
AJ
.
Glycaemic control in type 1 diabetes during real time continuous glucose monitoring compared with self monitoring of blood glucose: meta-analysis of randomised controlled trials using individual patient data
.
BMJ
2011
;
343
:
d3805
6.
Schwandt
A
,
Hermann
JM
,
Rosenbauer
J
, et al.;
DPV Initiative
.
Longitudinal trajectories of metabolic control from childhood to young adulthood in type 1 diabetes from a large German/Austrian registry: a group-based modeling approach
.
Diabetes Care
2017
;
40
:
309
316
7.
Foster
NC
,
Beck
RW
,
Miller
KM
, et al
.
State of type 1 diabetes management and outcomes from the T1D Exchange in 2016-2018
.
Diabetes Technol Ther
2019
;
21
:
66
72
8.
Birkebaek
NH
,
Hermann
JM
,
Hanberger
L
, et al
.
Center size and glycemic control: an international study with 504 centers from seven countries
.
Diabetes Care
2019
;
42
:
e37
e39
9.
Šoupal
J
,
Petruželková
L
,
Grunberger
G
, et al
.
Glycemic outcomes in adults with T1D are impacted more by continuous glucose monitoring than by insulin delivery method: 3 years of follow-up from the COMISAIR study
.
Diabetes Care
2020
;
43
:
37
43
10.
Sherr
JL
,
Hermann
JM
,
Campbell
F
, et al.;
T1D Exchange Clinic Network, the DPV Initiative, and the National Paediatric Diabetes Audit and the Royal College of Paediatrics and Child Health registries
.
Use of insulin pump therapy in children and adolescents with type 1 diabetes and its impact on metabolic control: comparison of results from three large, transatlantic paediatric registries
.
Diabetologia
2016
;
59
:
87
91
11.
DeSalvo
DJ
,
Miller
KM
,
Hermann
JM
, et al.;
T1D Exchange and DPV Registries
.
Continuous glucose monitoring and glycemic control among youth with type 1 diabetes: international comparison from the T1D Exchange and DPV Initiative
.
Pediatr Diabetes
2018
;
19
:
1271
1275
12.
Danne
T
,
Lion
S
,
L
, et al.;
SWEET Group
.
Criteria for Centers of Reference for pediatric diabetes--a European perspective
.
Pediatr Diabetes
2012
;
13
(
Suppl. 16
):
62
75
13.
World Bank
.
GDP per capita (current US$) . 14. World Bank . Domestic general government health expenditure per capita (current US$)
.
Accessed 2 November 2020. Available from https://data.worldbank.org/indicator/SH.XPD.GHED.PC.CD
15.
American Diabetes Association; European Association for the Study of Diabetes; International Federation of Clinical Chemistry and Laboratory Medicine; International Diabetes Federation
.
Consensus statement on the worldwide standardisation of the HbA1c measurement
.
Diabetologia
2007
;
50
:
2042
2043
16.
Abraham
MB
,
Jones
TW
,
Naranjo
D
, et al
.
ISPAD Clinical Practice Consensus Guidelines 2018: assessment and management of hypoglycemia in children and adolescents with diabetes
.
Pediatr Diabetes
2018
;
19
(
Suppl. 27
):
178
192
17.
Wolfsdorf
JI
,
Glaser
N
,
Agus
M
, et al
.
ISPAD Clinical Practice Consensus Guidelines 2018: diabetic ketoacidosis and the hyperglycemic hyperosmolar state
.
Pediatr Diabetes
2018
;
19
(
Suppl. 27
):
155
177
18.
Rewers
MJ
,
Pillay
K
,
de Beaufort
C
, et al.;
International Society for Pediatric and Adolescent Diabetes
.
ISPAD Clinical Practice Consensus Guidelines 2014. Assessment and monitoring of glycemic control in children and adolescents with diabetes
.
Pediatr Diabetes
2014
;
15
(
Suppl. 20
):
102
114
19.
DiMeglio
LA
,
Acerini
CL
,
Codner
E
, et al
.
ISPAD Clinical Practice Consensus Guidelines 2018: glycemic control targets and glucose monitoring for children, adolescents, and young adults with diabetes
.
Pediatr Diabetes
2018
;
19
(
Suppl. 27
):
105
114
20.
Szypowska
A
,
Schwandt
A
,
Svensson
J
, et al.;
SWEET Study Group
.
Insulin pump therapy in children with type 1 diabetes: analysis of data from the SWEET registry
.
Pediatr Diabetes
2016
;
17
(
Suppl. 23
):
38
45
21.
van den Boom
L
,
Karges
B
,
Auzanneau
M
, et al
.
Temporal trends and contemporary use of insulin pump therapy and glucose monitoring among children, adolescents, and adults with type 1 diabetes between 1995 and 2017
.
Diabetes Care
2019
;
42
:
2050
2056
22.
Skrivarhaug
T
,
Kummernes
SJ
,
Kamaleri
Y
.
The Norwegian Childhood Diabetes Registry (NCDR) Annual Report 2018
.
Oslo, Norway
,
Oslo universitetssykehus
,
2019
.
23.
Hanberger
L
,
Samuelsson
U
,
Holl
RW
,
Fröhlich-Reiterer
E
,
Åkesson
K
,
Hofer
S
.
Type 1 diabetes during adolescence: international comparison between Germany, Austria, and Sweden
.
Pediatr Diabetes
2018
;
19
:
506
511
24.
National Paediatric Diabetes Audit, Royal College of Paediatrics and Child Health
.
National Paediatric Diabetes Audit 2017/18
,
2019
.
25.
Gajewska
KA
,
Bennett
K
,
Biesma
R
,
Sreenan
S
.
Low uptake of continuous subcutaneous insulin infusion therapy in people with type 1 diabetes in Ireland: a retrospective cross-sectional study
.
BMC Endocr Disord
2020
;
20
:
92
26.
Miller
KM
,
Hermann
J
,
Foster
N
, et al.;
T1D Exchange and DPV Registries
.
Longitudinal changes in continuous glucose monitoring use among individuals with type 1 diabetes: international comparison in the German and Austrian DPV and U.S. T1D Exchange registries
.
Diabetes Care
2020
;
43
:
e1
e2
27.
Sumnik
Z
,
Szypowska
A
,
Iotova
V
, et al.;
SWEET Study Group
.
Persistent heterogeneity in diabetes technology reimbursement for children with type 1 diabetes: the SWEET perspective
.
Pediatr Diabetes
2019
;
20
:
434
443
28.
P
,
A
,
Buckingham
BA
,
Wilson
DM
,
Maahs
DM
.
Sustained continuous glucose monitor use in low-income youth with type 1 diabetes following insurance coverage supports expansion of continuous glucose monitor coverage for all
.
Diabetes Technol Ther
2018
;
20
:
632
634
29.
Haynes
A
,
Hermann
JM
,
Clapin
H
, et al.;
WACDD and DPV Registries
.
Decreasing trends in mean HbA1c are not associated with increasing rates of severe hypoglycemia in children: a longitudinal analysis of two contemporary population-based pediatric type 1 diabetes registries from Australia and Germany/Austria between 1995 and 2016
.
Diabetes Care
2019
;
42
:
1630
1636
30.
Ravi
SJ
,
Coakley
A
,
Vigers
T
,
Pyle
L
,
Forlenza
GP
,
Alonso
T
.
Pediatric Medicaid patients with type 1 diabetes benefit from continuous glucose monitor technology
.
J Diabetes Sci Technol
.
14 March 2020 [Epub ahead of print]. DOI:10.1177/1932296820906214
31.
Johnson
SR
,
Cooper
MN
,
Davis
EA
,
Jones
TW
.
Hypoglycaemia, fear of hypoglycaemia and quality of life in children with type 1 diabetes and their parents
.
Diabet Med
2013
;
30
:
1126
1131
32.
Sherr
JL
,
Tauschmann
M
,
Battelino
T
, et al
.
ISPAD Clinical Practice Consensus Guidelines 2018: diabetes technologies
.
Pediatr Diabetes
2018
;
19
(
Suppl. 27
):
302
325
33.
Charalampopoulos
D
,
Hermann
JM
,
Svensson
J
, et al
.
Exploring variation in glycemic control across and within eight high-income countries: a cross-sectional analysis of 64,666 children and adolescents with type 1 diabetes
.
Diabetes Care
2018
;
41
:
1180
1187
34.
Hermann
JM
,
Miller
KM
,
Hofer
SE
, et al.;
T1D Exchange Clinic Network and the DPV initiative
.
The transatlantic HbA 1c gap: differences in glycaemic control across the lifespan between people included in the US T1D Exchange Registry and those included in the German/Austrian DPV registry
.
Diabet Med
2020
;
37
:
848
855
35.
Witsch
M
,
Kosteria
I
,
Kordonouri
O
, et al.;
SWEET Group
.
Possibilities and challenges of a large international benchmarking in pediatric diabetology-The SWEET experience
.
Pediatr Diabetes
2016
;
17
(
Suppl. 23
):
7
15
36.
Mönkemöller
K
,
Müller-Godeffroy
E
,
Lilienthal
E
, et al
.
The association between socio-economic status and diabetes care and outcome in children with diabetes type 1 in Germany: the DIAS Study (diabetes and social disparities)
.
Pediatr Diabetes
2019
;
20
:
637
644
.
37.
Auzanneau
M
,
Lanzinger
S
,
Bohn
B
, et al.;
DPV Initiative
.
Area deprivation and regional disparities in treatment and outcome quality of 29,284 pediatric patients with type 1 diabetes in Germany: a cross-sectional multicenter DPV analysis
.
Diabetes Care
2018
;
41
:
2517
2525
38.
Nielsen
NF
,
Gaulke
A
,
Eriksen
TM
,
Svensson
J
,
Skipper
N
.
Socioeconomic inequality in metabolic control among children with type 1 diabetes: a nationwide longitudinal study of 4,079 Danish children
.
Diabetes Care
2019
;
42
:
1398
1405
39.
Maahs
DM
,
Hermann
JM
,
Holman
N
, et al.;
National Paediatric Diabetes Audit and the Royal College of Paediatrics and Child Health, the DPV Initiative, and the T1D Exchange Clinic Network
.
Rates of diabetic ketoacidosis: international comparison with 49,859 pediatric patients with type 1 diabetes from England, Wales, the U.S., Austria, and Germany
.
Diabetes Care
2015
;
38
:
1876
1882
40.
Karges
B
,
Schwandt
A
,
Heidtmann
B
, et al
.
Association of insulin pump therapy vs insulin injection therapy with severe hypoglycemia, ketoacidosis, and glycemic control among children, adolescents, and young adults with type 1 diabetes
.
JAMA
2017
;
318
:
1358
1366
41.
Weinzimer
SA
,
Ahern
JH
,
Doyle
EA
, et al
.
Persistence of benefits of continuous subcutaneous insulin infusion in very young children with type 1 diabetes: a follow-up report
.
Pediatrics
2004
;
114
:
1601
1605
42.
Hanas
R
,
Lindgren
F
,
B
.
A 2-yr national population study of pediatric ketoacidosis in Sweden: predisposing conditions and insulin pump use
.
Pediatr Diabetes
2009
;
10
:
33
37
43.
Tauschmann
M
,
Hermann
JM
,
Freiberg
C
, et al.;
DPV Initiative
.
Reduction in diabetic ketoacidosis and severe hypoglycemia in pediatric type 1 diabetes during the first year of continuous glucose monitoring: a multicenter analysis of 3,553 subjects from the DPV registry
.
Diabetes Care
2020
;
43
:
e40
e42
44.
Pettus
J
,
Price
DA
,
Edelman
SV
.
How patients with type 1 diabetes translate continuous glucose monitoring data into diabetes management decisions
.
Endocr Pract
2015
;
21
:
613
620